Style transfer strategy for developing a generalizable deep learning application in digital pathology

Shin, Seo Jeong; You, Seng Chan; Jeon, Hokyun; Jung, Ji Won; An, Min Ho; Park, Rae Woong; Roh, Jin

doi:10.1016/j.cmpb.2020.105815

Cited by 31 publications

(9 citation statements)

References 29 publications

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“…anks to the rise of deep learning, the study in [10] first discovered that pretrained convolutional neural network models could be used as feature extractors to extract abstract features of images and then separate and reorganize them with stunning artistic results; the study in [11] used Gatys et al's feature extractor as the core part of the objective function of the feedforward network, while maintaining the same image migration effect, resulting in a computational efficiency improvement of three orders of magnitude. Based on this, the study in [12] suggested that it is redundant to train some images with similar styles separately and thus proposed training the same type of images together after normalization and also combining multiple styles of images together at the same time.…”

Section: Related Workmentioning

confidence: 99%

Digital Image Art Style Transfer Algorithm and Simulation Based on Deep Learning Model

2022

Scientific Programming

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In order to solve the problems of poor region delineation and boundary artifacts in Chinese style migration of images, an improved Variational Autoencoder (VAE) method for dress style migration is proposed. Firstly, the Yolo v3 model is used to quickly identify the dress localization of the input image, then, the classical semantic segmentation algorithm (FCN) is used to finely delineate the desired dress style migration region twice, and finally, the trained VAE model is used to generate the migrated Chinese style image. The results show that, compared with the traditional style migration model, the improved VAE style migration model can obtain finer synthetic images for dress style migration and can adapt to different Chinese traditional styles to meet the application requirements of dress style migration scenarios. We evaluated several deep learning-based models and achieved a BLEU value of 0.6 on average. The transformer-based model outperformed the other models, achieving a BLEU value of up to 0.72.

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Section: Related Workmentioning

confidence: 99%

Digital Image Art Style Transfer Algorithm and Simulation Based on Deep Learning Model

2022

Scientific Programming

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“…Thanks to the rise of deep learning, [10] rst discovered that pre-trained convolutional neural network models could be used as feature extractors to extract abstract features of images, and then separate and reorganize them with stunning artistic results, [11] used Gatys et al's feature extractor as the core part of the objective function of the feedforward network, while maintaining the same image migration effect. resulting in a computational e ciency improvement of three orders of magnitude.…”

Section: Related Workmentioning

confidence: 99%

Digital Image Art Style Transfer Algorithm and Simulation Based on Deep Learning Model

Kumar

2022

Preprint

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In order to solve the problems of poor region delineation and boundary artifacts in Indian style migration of images, an improved Variational Autoencoder (VAE) method for dress style migration is proposed. Firstly, the Yolo v3 model is used to quickly identify the dress localization of the input image, and then the classical semantic segmentation algorithm (FCN) is used to finely delineate the desired dress style migration region twice, and finally the trained VAE model is used to generate the migrated Indian style image using a decision support system. The results show that, compared with the traditional style migration model, the improved VAE style migration model can obtain finer synthetic images for dress style migration, and can adapt to different Indian traditional styles to meet the application requirements of dress style migration scenarios. We evaluated several deep learning based models and achieved BLEU value of 0.6 on average. The transformer-based model outperformed the other models, achieving a BLEU value of up to 0.72.

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“…Theoretical Basis. In the field of image processing, it is possible to use the cyclic consistent generated adversarial network to convert two image sample domains (nonpaired image domains) with large differences in style and improve the effect of nonpaired image style transfer [26]. Figure 2 shows the cycle-consistent generated adversarial network model.…”

Section: The Construction Of a Cyclic Consistentmentioning

confidence: 99%

A Cyclic Consistency Motion Style Transfer Method Combined with Kinematic Constraints

et al. 2021

Journal of Sensors

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Motion capture technology plays an important role in the production field of film and television, animation, etc. In order to reduce the cost of data acquisition and improve the reuse rate of motion capture data and the effect of movement style migration, the synthesis technology of motion capture data in human movement has become a research hotspot in this field. In this paper, kinematic constraints (KC) and cyclic consistency (CC) network are employed to study the methods of kinematic style migration. Firstly, cycle-consistent adversarial network (CCycleGAN) is constructed, and the motion style migration network based on convolutional self-encoder is used as a generator to establish the cyclic consistent constraint between the generated motion and the content motion, so as to improve the action consistency between the generated motion and the content motion and eliminate the lag phenomenon of the generated motion. Then, kinematic constraints are introduced to normalize the movement generation, so as to solve the problems such as jitter and sliding step in the movement style migration results. Experimental results show that the generated motion of the cyclic consistent style transfer method with kinematic constraints is more similar to the style of style motion, which improves the effect of motion style transfer.

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Style transfer strategy for developing a generalizable deep learning application in digital pathology

Cited by 31 publications

References 29 publications

Digital Image Art Style Transfer Algorithm and Simulation Based on Deep Learning Model

Digital Image Art Style Transfer Algorithm and Simulation Based on Deep Learning Model

Digital Image Art Style Transfer Algorithm and Simulation Based on Deep Learning Model

A Cyclic Consistency Motion Style Transfer Method Combined with Kinematic Constraints

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